def convert(results, garbage):
N = len(results.get('struct'))
clusters = [[]] + list(results.get('clusters'))
ntree = neutree.Neutree(
structs = results.get('struct'),
phis = results.get('phi'),
counts = results.get('count'),
logscores = results.get('llh'),
clusterings = [clusters for idx in range(N)],
garbage = garbage,
)
return ntree
def write_neutree(results, garbage, neutree_fn):
adjms = [R['adjm'] for R in results]
structs = [util.convert_adjmatrix_to_parents(A) for A in adjms]
llhs = [R['llh'] for R in results]
clusterings = [R['clusters'] for R in results]
phis = [R['phi'] for R in results]
N = len(structs)
counts = np.ones(N)
ntree = neutree.Neutree(
structs=structs,
phis=phis,
counts=counts,
logscores=llhs,
clusterings=clusterings,
garbage=garbage,
)
neutree.save(ntree, neutree_fn)
def convert(sampid, params_fn, trees_fn, neutree_fn):
adjms, llhs, phis, clusterings = pastri_util.load_results(
sampid, params_fn, trees_fn)
if len(adjms) == 0:
return
structs = [util.convert_adjmatrix_to_parents(A) for A in adjms]
N = len(structs)
params = inputparser.load_params(params_fn)
ntree = neutree.Neutree(
structs=structs,
phis=phis,
counts=np.ones(N),
logscores=llhs,
clusterings=clusterings,
garbage=params['garbage'],
)
neutree.save(ntree, neutree_fn)
def convert(params_fn, calder_mats_fn, calder_trees_fn, neutree_fn):
params = inputparser.load_params(params_fn)
mats, row_labels, col_labels = _load_mats(calder_mats_fn)
assert row_labels['Fhat'][0] == 'samples'
svids = row_labels['Fhat'][1:]
assert svids == common.sort_vids(svids)
struct = _load_struct(svids, calder_trees_fn)
ntree = neutree.Neutree(
structs=[struct],
phis=[mats['Fhat']],
counts=np.array([1]),
logscores=np.array([0.]),
clusterings=[params['clusters']],
garbage=params['garbage'],
)
neutree.save(ntree, neutree_fn)
def convert_results(results, base_clusters, garbage, use_supervars):
tidxs = sorted(results.tree_summary.keys())
llhs = np.array([results.tree_summary[tidx]['llh'] for tidx in tidxs])
adjms = []
phis = []
clusterings = []
for idx, (tidx, mutass) in enumerate(results.load_all_mut_assignments()):
# Ensure trees are provided in sorted order.
assert idx == tidx
if use_supervars:
full_mutass = replace_supervar_with_variants(base_clusters, mutass)
else:
full_mutass = {cid: mutass[cid]['ssms'] for cid in mutass.keys()}
adjm = common.convert_adjlist_to_adjmatrix(
results.tree_summary[tidx]['structure'])
phi = extract_phi(results.tree_summary[tidx]['populations'])
pwgs_clusters = [
full_mutass[cidx] if cidx in full_mutass else []
for cidx in range(len(adjm))
]
adjms.append(adjm)
phis.append(phi)
clusterings.append(pwgs_clusters)
structs = [util.convert_adjmatrix_to_parents(A) for A in adjms]
counts = np.ones(len(structs))
ntree = neutree.Neutree(
structs=structs,
phis=phis,
counts=counts,
logscores=llhs,
clusterings=clusterings,
garbage=garbage,
)
return ntree